Face recognition technology has received increased attention lately, since it can be used in various applications such as surveillance, security, advertising, and the like. However, previous attempts to develop efficient face recognition systems were not successful because the computers and algorithms used in previous face recognition systems could not effectively handle the huge amount of data and complicated computation inherently involved in face recognition. These previous attempts typically utilized simple feature representations that do not account for intrinsic structure information in face images. Such intrinsic structure information can only be encoded by using advanced methods such as higher order statistics. Furthermore, previous face recognition systems did not work well when the face images are illuminated under different lighting conditions.
Recently, linear subspace methods such as Principal Component Analysis (“PCA”) and Fisher Linear Discriminant (“FLD”) have been applied to face recognition with impressive results. PCA and FLD utilize the basic eigenvalue problem in face recognition and hence induce a lower dimensional representation of the face images from their image samples in the input space. In this manner, PCA and FLD reduce the amount of data and hence alleviate the computational burden in face recognition.
One example of a face recognition system using PCA is disclosed in U.S. Pat. No. Re. 36,041 to Turk et al. that is incorporated by reference herein in its entirety. Here, the face recognition system utilizes PCA to obtain a representation of the face images in a multi-dimensional space lower in dimension than the input space. The use of PCA enables reduction of the amount of data and the computational burden of face recognition.
One of the disadvantages of PCA and FLD is that the lower dimensional representation of the face images has no information regarding the relationship between the pixels in the image except the relative position between the pixels. That is, the lower dimensional representations in PCA or FLD are based on second order statistics of the images, i.e., pixelwise covariance among the pixels, and do not address higher order statistical dependencies such as the relationships among three or more pixels. Such higher order dependencies in a face image may include relations among pixel intensity values, such as the relations among three or more pixels in an edge or curve. The higher order dependencies often have more meaningful, representative features of the face image and may capture important information for face recognition. One of the reasons why PCA and FLD do not use higher order statistical dependencies is that it results in a tremendous computational burden.
Some research has been done to use higher order statistical dependencies in the machine learning area. However, the input data used in machine learning is quite different from the face image data used in face recognition. First, data in machine learning is relatively clean (without much noise) and have low dimensionality, i.e., each sample or data point is typically a short vector with less than 200 elements. Alternatively, the variations of face images are large, which is one of the reasons why face recognition is difficult to implement. Second, the samples in face recognition have dimensionality much higher than machine learning, which results in an enormous amount of data and computational burden in face recognition. For example, a typical 50×50 pixel face image has 2500 elements in each sample. For these reasons, the algorithm and mathematics involved in using higher order statistical dependencies in the machine learning area are inherently different from those used in face recognition. Therefore, the algorithm and mathematics for using higher order statistical dependencies in the machine learning area is not applicable to face recognition.
Therefore, it is necessary to have a face recognition system and method that can process face image data having wide variations and an enormous amount of image data such that higher order dependencies of the face image can be used to obtain more representative features of the face image without introducing a huge computational burden on the face recognition system. In addition, what is needed is a face recognition system that utilizes the discriminant features of the face images and maximizes the class separation when these features are projected to a lower dimensional face image space.